Patient setup error is one of the major causes of target position uncertainty in external radiotherapy for extracranial targets. The efficacy of radiation therapy treatment depends on the patient setup accuracy at each daily fraction. A significant problem is reproducing the patient position during treatment planning for every fraction of the treatment process. Uncertainty of target position and shape can result in decreased radiation dose to the target and an increased dose to the surrounding normal tissues. To compensate for the uncertainty of the target position and shape in irradiation process the planning target volume (PTV) must have a larger margin compared to static targets with the same clinical target volume (CTV). This approach increases the probability that the target will receive a lethal dose of radiation. Unfortunately, it also increases collateral damage to surrounding healthy tissues. Efforts have been made in recent years to develop dedicated systems using image registration technology for patient position verification in radiotherapy.
U.S. Patent Application Publication No. 2005/0272991 (Xu et al.) is directed to a system and method for registering pre-operative images of an object with an intra-operative image of the object. Prior to an operative procedure, digitally reconstructed radiographs (DRRs) are generated for the pre-operative images of each individual patient. Signatures are extracted from the DRRs. The signatures are stored in a knowledge base. During the operative procedure, a signature is extracted from the intra-operative image. The intra-operative signature is compared to the stored pre-operative signatures. A pre-operative image having a best signature match to the intra-operative signature is retrieved. The retrieved pre-operative image is registered with the intra-operative image.
U.S. Patent Application Publication No. 2006/0002631 A1 (Fu et al.) discloses a system and method for automatically selecting a region of interest (ROI) within an image of an object to perform image registration between the image and another image of the object for tracking and aligning a treatment target. The ROI is determined by defining an entropy measure H of the image, and selecting the region within the image in which the entropy measure is maximized.
In '2631, anatomical reference structures, for example, skeletal or vertebral structures that are rigid and easily visible in diagnostic x-ray images, are used as reference points.
Numerous image registration techniques for medical applications have been reported recently in academic research. Those techniques are generally fine tuned for specific cases (specific body parts, image acquisition conditions, etc.). For instance, Vos et al., introduces an image registration scheme for prostate treatments based on local extremum lines emanating from bone ridges in portal image and digitally reconstructed radiographs (refer to “Evaluation of an automatic system for simulator/portal image matching”, F. M. Vos et al., MICCAI 2000, LNCS 1935, pp. 442-451, Springer-Verlag Berlin Heidelberg 2000).
Ali Khamene et al. (see “Automatic registration of portal images and volumetric CT for patient positioning in radiation therapy”, Medical Image Analysis pp. 1-17) proposes an intensity-based automatic registration method using multiple portal images and a pre-treatment CT volume. Khamene et al. performs both geometric (e.g. scaling) and radiometric (e.g. contrast) calibrations to generate digitally reconstructed radiographs (DRRs) that can be compared against portal images acquired right before treatment dose delivery. Simple similarity measure such as local normalized correlation (LNC) is used.
In practice, an image fusion system should accommodate a wide range of images in terms of intensities and contrast levels. People skilled in the art understand that the feasibility of extracting meaningful features from low intensity and low contrast images is very questionable, let alone the cases for which no bony structures exist. Without distinct and matched features for DRR and portal image, correct image retrieval and registration using feature-based approach become impossible.
A flexible image fusion system should also accommodate reference images (usually, 2D or 3D images obtained in radiotherapy planning stage) generated with or without the requirement of geometric and radiometric calibrations.
Therefore, an improved general approach of image fusion for patient setup error estimation is needed. The present invention is designed to overcome the problems set forth above.